NIMBL - Devlog #6

I noticed that the last few devlogs were quite lazy and to be frank trash. So this section is just getting you upto date. Explaining the problem, goal, benchmarks and architecture and just giving you a short summary.

What is NIMBL?

NIMBL which stands for nimble which describes a person as being fast, agile and efficent.

Nimbl’s goal is it to allow users to get a coding harness which is half the cost and dobule as fast and allows for them to keep around 80% of the intelligence.

It also includes 4 modes learn,explain,plan and build. Plan and build are just the typical modes you see in all harness like opencode and explain is a copy of plan but adjusted, so it shows examples and is able to retrieve the codebase ligthning fast and offer detailed explanation. Learn is the same as build but with measurements in place so you cant just prompt it rather it give you example code or code with simple mistakes and you have to fix them yourself.

What have I done?

So since fully porting the Opencode tui I have decided to work on nimbles effectivnes I first starded with indepth analysis of other harness and how they work their benefits and their drawbacks. So for example how Pi is ligthweight or how Aiders gathers context. Afterwards I summarized everythign i learned and implemented it, and then came the benchmarks i first started with a local 75 test benchmark across all kinds of test from lh, mf to retrival tasks on which nimbl performed great with being 3x cheaper and achieving a 98% solve rate.

Benchmarks

The next step was running NIMBL on offical benchmarks for that I choose SWE Lite as it one of the more reputable ones and smaller ones. OF course I wouldnt be able to run even that size as it around 120GB and I dont have that much money for benchmarks. Just so you know evey benchmark I have run used openrouter credits and Iam the person paying for them. So i sliced it, Slicing means instead of running all you take out a certain part and download only that and run only that.
On that slice NIMBL was miserable solving 1 out of 15 task. After further optimizing the logic behind it NIMBL is now at a point where it reaches around 80% accuracy and is a third of the price.

How does NIMBL work?

NIMBL's main architecture is built around a layered context and execution pipeline.

The project is indexed using lexical search, structural parsing, and dependency-graph relationships.

When a task is received, NIMBL classifies it and retrieves only the most relevant files, symbols, and related dependencies.

The retrieved context is compressed and placed into a stable prompt structure, allowing provider-side caching to reduce cost and latency.

The agent then works through the task using tools, tests, diagnostics, and bounded execution loops.

The result is tracked through token usage, cache hits, cost, latency, and success metrics.

This allows NIMBL to stay lightweight while still having access to the important parts of a codebase. The goal is not to send less context blindly, but to send better context.

Combined with the different learning modes, this gives NIMBL the foundation to become a faster, cheaper, and more educational coding harness.

in the screenshot you can see how much credits i got left i had 10 at the start of the development. Hopefully this made NIMBL easier to understand and gave a short overview